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feat: 新增物料/入库单实时 CLIP 向量提取（新建+更新），修复 I/O 延迟和路径解析静默失败

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This commit is contained in:
DXC
2026-05-25 10:04:32 +08:00
parent ee9b19e72a
commit 1da4b454cd
10 changed files with 100 additions and 3 deletions
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4
inventory-backend/app/models/base.py
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@ -1,5 +1,6 @@
# app/models/base.py
from app.extensions import db
from pgvector.sqlalchemy import Vector
import json
@ -34,6 +35,9 @@ class MaterialBase(db.Model):
# 强制质检标记（采购入库时必须上传检测报告）
is_inspection_required = db.Column(db.Boolean, default=False, comment='是否强制要求质检')
# CLIP 视觉向量（用于以图搜图）
img_embedding = db.Column(Vector(512), nullable=True)
# ============================================================
# 关联关系区域
# ============================================================

4
inventory-backend/app/models/inbound/buy.py
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@ -1,5 +1,6 @@
# inventory-backend/app/models/inbound/buy.py
from app.extensions import db
from pgvector.sqlalchemy import Vector
import json
# 显式导入 MaterialBase 以防 relationship 找不到引用
from app.models.base import MaterialBase
@ -55,6 +56,9 @@ class StockBuy(db.Model):
# 全局打印流水号
global_print_id = db.Column(db.Integer)
# CLIP 视觉向量（用于以图搜图）
arrival_image_embedding = db.Column(Vector(512), nullable=True)
# 关系定义
base = db.relationship('MaterialBase', back_populates='stock_buys')

4
inventory-backend/app/models/inbound/product.py
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@ -1,5 +1,6 @@
# app/models/inbound/product.py
from app.extensions import db
from pgvector.sqlalchemy import Vector
import json
from app.models.base import MaterialBase
@ -58,6 +59,9 @@ class StockProduct(db.Model):
# 全局打印流水号
global_print_id = db.Column(db.Integer)
# CLIP 视觉向量（用于以图搜图）
arrival_image_embedding = db.Column(Vector(512), nullable=True)
# 关系定义
base = db.relationship('MaterialBase', back_populates='stock_products')

4
inventory-backend/app/models/inbound/semi.py
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@ -1,5 +1,6 @@
# app/models/inbound/semi.py
from app.extensions import db
from pgvector.sqlalchemy import Vector
import json
from app.models.base import MaterialBase
@ -56,6 +57,9 @@ class StockSemi(db.Model):
# 全局打印流水号
global_print_id = db.Column(db.Integer)
# CLIP 视觉向量（用于以图搜图）
arrival_image_embedding = db.Column(Vector(512), nullable=True)
# 关系定义
base = db.relationship('MaterialBase', back_populates='stock_semis')

9
inventory-backend/app/services/inbound/base_service.py
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@ -12,6 +12,7 @@ import traceback
import json
import io
import datetime
from app.utils.ai_vision import extract_and_embed
# 需要 pip install openpyxl
from openpyxl import Workbook
from openpyxl.styles import Font, Alignment, Border, Side, PatternFill
@ -555,7 +556,9 @@ class MaterialBaseService:
product_image=json.dumps(data.get('generalImage', [])),
is_enabled=is_enabled_val
)
# 实时提取产品图向量（失败不影响业务）
if new_material.product_image:
new_material.img_embedding = extract_and_embed(new_material.product_image)
db.session.add(new_material)
db.session.commit()
return new_material
@ -587,6 +590,10 @@ class MaterialBaseService:
if 'generalImage' in data:
material.product_image = json.dumps(data['generalImage'])
# 补上这两行：提取新上传图片的向量！
if material.product_image:
material.img_embedding = extract_and_embed(material.product_image)
# 【核心修改】：兼容前端传来的布尔值
if 'isEnabled' in data:
raw_enabled = data['isEnabled']

4
inventory-backend/app/services/inbound/buy_service.py
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@ -9,6 +9,7 @@ from sqlalchemy import or_, func, text, and_
from sqlalchemy.exc import IntegrityError
import traceback
import json
from app.utils.ai_vision import extract_and_embed
class BuyInboundService:
@ -177,6 +178,9 @@ class BuyInboundService:
arrival_photo=json.dumps(data.get('arrival_photo', [])),
inspection_report=json.dumps(data.get('inspection_report', []))
)
# 实时提取到货图片向量（失败不影响业务）
if new_stock.arrival_photo:
new_stock.arrival_image_embedding = extract_and_embed(new_stock.arrival_photo)
db.session.add(new_stock)
db.session.commit()
return new_stock

4
inventory-backend/app/services/inbound/product_service.py
View File

@ -9,6 +9,7 @@ from sqlalchemy import or_, func, text, and_
from sqlalchemy.exc import IntegrityError
import traceback
import json
from app.utils.ai_vision import extract_and_embed
class ProductInboundService:
@ -183,6 +184,9 @@ class ProductInboundService:
sale_price=float(data.get('sale_price') or 0),
order_id=data.get('order_id')
)
# 实时提取成品实拍图向量（失败不影响业务）
if new_stock.product_photo:
new_stock.arrival_image_embedding = extract_and_embed(new_stock.product_photo)
db.session.add(new_stock)
db.session.commit()
return new_stock

4
inventory-backend/app/services/inbound/semi_service.py
View File

@ -9,6 +9,7 @@ from sqlalchemy import or_, func, text, and_
from sqlalchemy.exc import IntegrityError
import traceback
import json
from app.utils.ai_vision import extract_and_embed
class SemiInboundService:
@ -220,6 +221,9 @@ class SemiInboundService:
detail_link=data.get('detail_link'),
remark=data.get('remark')
)
# 实时提取到货图片向量（失败不影响业务）
if new_stock.arrival_photo:
new_stock.arrival_image_embedding = extract_and_embed(new_stock.arrival_photo)
db.session.add(new_stock)
db.session.commit()
return new_stock

60
inventory-backend/app/utils/ai_vision.py
View File

@ -4,6 +4,8 @@ AI Vision 模块 - CLIP Vision Encoder ONNX 推理
"""
import os
import json
import time
import numpy as np
from PIL import Image
import onnxruntime as ort
@ -130,3 +132,61 @@ def get_image_embedding(image_path: str) -> list:
}
)
return outputs[0][0].tolist()
# ============================================================================
# 通用向量提取工具：防呆、防错
# ============================================================================
def extract_and_embed(photo_source):
if not photo_source:
return None
try:
# 1. 提取基础字符串
photo_source_str = str(photo_source).strip()
raw_path = ""
# 尝试剥掉 JSON 外壳
try:
parsed = json.loads(photo_source_str)
if isinstance(parsed, list):
raw_path = parsed[0] if parsed else ""
elif isinstance(parsed, str):
raw_path = parsed
else:
raw_path = str(parsed)
except:
raw_path = photo_source_str
if not raw_path:
return None
# 2. 剥离出最纯净的文件名 (只取最后一段)
pure_filename = raw_path.split('/')[-1]
# 3. 【终极物理净化】强行抠掉所有多余的标点符号！
# 哪怕传进来的是 123.jpg"] 或者是 "123.jpg"，全部洗干净
pure_filename = pure_filename.replace('"', '').replace("'", "").replace('[', '').replace(']', '')
# 4. 拼接真实的 Docker 物理路径
file_path = os.path.join('/app/uploads', pure_filename)
# 5. 加入重试机制 (最多等 3 秒)
max_retries = 6
for i in range(max_retries):
if os.path.exists(file_path):
# 文件找到了，开始提取向量
vec = get_image_embedding(file_path)
if isinstance(vec, np.ndarray):
return vec.tolist()
return vec
else:
print(f"[AI 识图等待] 第 {i+1} 次尝试，未找到文件 {file_path}，等待 0.5s...")
time.sleep(0.5)
print(f"[AI 识图警告] 彻底失败！经过等待依然未找到图片: {file_path}")
except Exception as e:
print(f"[AI 识图错误] 实时提取向量失败: {str(e)}")
return None

2
inventory-backend/requirements.txt
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@ -28,3 +28,5 @@ APScheduler==3.10.4
pytz
# [新增] 进度条库 (脚本和任务所需)
tqdm>=4.66.0
# [新增] pgvector 向量数据库支持（以图搜图 / 实时向量提取）
pgvector>=0.2.0